2016 Poster Sessions : Optimal Decision Making for Automotive Safety Systems

Student Name : Tim Wheeler
Advisor : Mykel J. Kochenderfer
Research Areas: Artificial Intelligence
Abstract:
As the automotive industry moves towards autonomous driving, it becomes increasingly necessary to develop advanced collision avoidance systems, crash prediction systems, and the tools for their rigorous analysis. The certification of any automated driving system will require a combination of driving tests and detailed simulation studies to ensure system effectiveness and safety. Driving tests are inherently expensive and time-intensive; simulation can be used to quickly and inexpensively test over the space of potential trajectories. The Stanford Intelligent Systems Laboratory researches algorithms and analytical methods for deriving optimal decision strategies for automotive safety systems which account for uncertainty in the fast-paced, multi-agent automotive environment.

Bio:
Tim Allan Wheeler is a graduate student of Aeronautics and Astronautics at Stanford University. He is a Ph.D. candidate under the mentorship of Prof. Mykel Kochenderfer of the Stanford Intelligent Systems Laboratory, applying decision making theory to the problem of automotive collision avoidance. Tim's research focuses on autonomous cars, particularly in designing robust collision avoidance algorithms and the tools for their rigorous analysis.